In-Depth Genetic Diversity and Population Structure of Endangered Peruvian Amazon Rosewood Germplasm Using Genotyping by Sequencing (GBS) Technology

Article In-Depth Genetic Diversity and Population Structure of Endangered Peruvian Amazon Rosewood Germplasm Using Genotyping by Sequencing (GBS) Technology

Muhammad Azhar Nadeem 1,† , Stalin Juan Vasquez Guizado 2,† , Muhammad Qasim Shahid 3 , Muhammad Amjad Nawaz 4 , Ephrem Habyarimana 5 , Sezai Erci¸sli 6 , Fawad Ali 7, Tolga Karaköy 1, Muhammad Aasim 1, Rü¸stüHatipo˘glu 8, Juan Carlos Castro Gómez 2 , Jorge Luis Marapara del Aguila 2, Pedro Marcelino Adrianzén Julca 2, Esperanza Torres Canales 2 , Seung Hwan Yang 9 , Gyuhwa Chung 9,* and Faheem Shehzad Baloch 1,*

1 Faculty of Agricultural Sciences and Technologies, Sivas University of Science and Technology, Sivas 58140, Turkey; [email protected] (M.A.N.); [email protected] (T.K.); [email protected] (M.A.) 2 Specialized Unit of Biotechnology, Research Center of Natural Resources of the Amazon, National University of the Peruvian Amazon, Iquitos 1600, Peru; [email protected] (S.J.V.G.); [email protected] (J.C.C.G.); [email protected] (J.L.M.d.A.); [email protected] (P.M.A.J.); [email protected] (E.T.C.) 3 State Key Laboratory for Conservation and Utilization of Subtropical Agro-Bio Resources, South China Agricultural University, Guangzhou 510642, China; [email protected] 4 Laboratory of Bio-Economics and Biotechnology, Department of Bio-Economics and Food Safety, School of Economics and Management, Far Eastern Federal University, 690950 Vladivostok, Russia; Citation: Nadeem, M.A.; [email protected] 5 Guizado, S.J.V.; Shahid, M.Q.; CREA Research Center for Cereal and Industrial Crops, 40128 Bologna, Italy; [email protected] Nawaz, M.A.; Habyarimana, E.; 6 Department of Horticulture, Faculty of Agriculture, Ataturk University, Erzurum 25240, Turkey; Erci¸sli,S.; Ali, F.; Karaköy, T.; [email protected] Aasim, M.; Hatipo˘glu,R.; et al. 7 Department of Plant Sciences, Quaid-I-Azam University, Islamabad 45710, Pakistan; [email protected] In-Depth Genetic Diversity and 8 Department of Field Crops, Faculty of Agricultural, University of Cukurova, Adana 01380, Turkey; Population Structure of Endangered [email protected] Peruvian Amazon Rosewood 9 Department of Biotechnology, Chonnam National University, Chonnam 59626, Korea; Germplasm Using Genotyping by [email protected] Sequencing (GBS) Technology. Forests * Correspondence: [email protected] (G.C.); [email protected] (F.S.B.); 2021, 12, 197. https://doi.org/ Tel.: +90-545-540-4239 (F.S.B.) 10.3390/f12020197 † These authors contributed equally to this work.

Received: 13 October 2020 Abstract: Research studies on conservative genetics of endangered plants are very important to Accepted: 2 February 2021 establish the management plans for the conservation of biodiversity. Rosewood is an evergreen tree Published: 8 February 2021 of the Amazon region and its essential oil has great acceptance in the medical and cosmetic industry. The present study aimed to explore the genetic diversity and population structure of 90 rosewood Publisher’s Note: MDPI stays neutral accessions collected from eight localities of Peruvian Amazon territory through DArTseq markers. with regard to jurisdictional claims in A total of 7485 informative markers resulted from genotyping by sequencing (GBS) analysis were published maps and institutional afﬁl- used for the molecular characterization of rosewood germplasm. Mean values of various calculated iations. diversity parameters like observed number of alleles (1.962), the effective number of alleles (1.669), unbiased expected heterozygosity (0.411), and percent polymorphism (93.51%) over the entire germplasm showed the existence of a good level of genetic variations. Our results showed that the Mairiricay population was more diverse compared to the rest of the populations. Tamshiyacu-2 Copyright: © 2021 by the authors. and Mairiricay-15 accessions were found genetically distinct accessions. The analysis of molecular Licensee MDPI, Basel, Switzerland. variance (AMOVA) reﬂected maximum variations (75%) are due to differences within populations. This article is an open access article The implemented clustering algorithms, i.e., STRUCTURE, neighbor-joining analysis and principal distributed under the terms and coordinate analysis (PCoA) separated the studied germplasm on the basis of their geographical conditions of the Creative Commons locations. Diversity indices for STRUCTURE-based populations showed that subpopulation A is more Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ diverse population than the rest of the populations, for such reason, individuals belonging to this 4.0/). subpopulation should be used for reintroduction or reinforcement plans of rosewood conservation.

Forests 2021, 12, 197. https://doi.org/10.3390/f12020197 https://www.mdpi.com/journal/forests Forests 2021, 12, 197 2 of 17

We envisage that molecular characterization of Peruvian rosewood germplasm with DArTseq markers will provide a platform for the conservation, management and restoration of endangered rosewood in upcoming years.

Keywords: Aniba rosaeodora; DArTseq; germplasm characterization; molecular markers; population genetics

1. Introduction The world’s flora and fauna are currently facing a huge loss of habitat which has reulted in the depletion of a number of populations, some leading to extinction [1]. The conservation of plant species has not received the required attention as compared to animals [2]. According to the information shared by the first global analysis of extinction risk in 2010, 25% of the world’s plant species are critically endangered [3]. Endangered species are known to have small or declining populations that experience the effects of inbreeding and genetic erosion resulting in high extinction risks [4]. The conservation genetic studies are considered vital for the preservation perspective of endangered species [5]. Previous research efforts have confirmed that both anthropogenic activities and climatic changes are becoming stronger than before, and are resulting in habitat fragmentation and/or population decline for a good number of endangered species [6,7]. By realizing these threats, it is very important to investigate the adaptive potential, genetic diversity and long-term conservation status of endangered plant species [8]. The Amazon region is considered one of the “richest reservoirs of biodiversity” and “most-varied biological reservoir”, containing several million species of insects, plants, birds [9]. Rosewood (Aniba rosaeodora Ducke) belongs to the family Lauraceae with diploid chromosomes number 2n = 24. Rosewood forests are present in Peru, Brazil, Colombia, Guyana, Venezuela and Suriname [10]. Indigenous peoples of the Amazon basin mostly used the rosewood to make canoes and as fuel. Rosewood essential oil is very popular, because it contains high contents of linalool. It is reported that 74.4–81.8% linalool content is present in leaves and branches of rosewood, while trunk wood contains ~100% linalool content [11]. From 1875 to 1975, extraction of essential oil was carried at the commercial scale which resulted in the significant depletion of natural rosewood stands [12]. After the depletion of rosewood natural stands, French Guiana prohibited the cutting of trees which resulted in a significant decrease in the export of essential oil. Presently, Brazil is the only producer and exporter of its essential oil [13]. Cutting of rosewood trees on large scale resulted in the complete depletion of rosewood forests from various regions of the Amazon. Currently, rosewood is included as an endangered species in the database of the Convention on International Trade in Endangered Species of Wild Fauna and Flora [14]. The variations in climate, altitude, latitude, soils and typography together make Peru home to a spectacular diversity of flora and fauna [15]. The north Marañon–Amazonas river axis, along the rivers Tiger, Napo and Putumayo in Peru, contains the rosewood stands [16]. Samuel Reggeroni, the owner of the Pucabarranca farm on the Napo River, started the rosewood trade very first time in Peru in 1941 by sending rosewood essential oil samples to Europe [16]. A rapid increase in rosewood essential oil trade was observed in Peru and other parts of the world in the 1950s, which resulted in fragmentation of habitats and deforestation resulting from the extraction of species of high timber value [14]. As a result of the fragmentation of habitats and deforestation, rosewood is now a vulnerable species in Peru [14]. To combat these issues, the Peruvian government has taken strong actions and the export of rosewood wood and its essential oil has been banned since 1972. Moreover, the establishment of rosewood plantations is suggested by the Peruvian Ministry of Agriculture in order to conserve this valuable species [14,16]. Germplasm characterization remains a fundamental and most important step in germplasm resource management and conservation and provides an opportunity to in- Forests 2021, 12, 197 3 of 17

vestigate the novel variations that can be helpful for the breeding perspective [17,18]. Assessment of genetic variation is considered a prerequisite to explore the genetic potential and efficient utilization of germplasm, and provides an opportunity to develop conservation approaches for the breeding of endangered species [19]. Investigation of genetic diversity within and among populations of endangered species facilitates the management and conservation of genetic resources, which could be an important milestone to minimize the genetic drift, extinction of a species, and conservation of genetic resources through germplasm collection [20]. The presence of high genetic diversity in a population can increase the possibility to pick up the most favorable material for breeding perspectives. Similarities or differences between individuals, populations or species are evaluated in genetic diversity studies using morphological attributes, genealogical data and, molecular characteristics [21]. Advancements in molecular marker technology have changed the fate of plant breeding by exploring the novel variations [22]. Therefore, it is highly suggested to screen the germplasm at allelic levels implementing molecular marker compared to morphological and biochemical markers and could be effectively utilized for germplasm conservation and improvement [23]. A good number of DNA markers have been developed reflecting various advantages and limitations [22]. However, Diversity Arrays Technology (DArT) attracted the attention of scientists in a short time as a robust, low cost, high throughput genome-wide method to investigate the polymorphism compared to hybridization and PCR-based markers [24]. Diversity array technology (DArT) markers have been developed under the platform of genotyping by sequencing (GBS) [25]. DArT analyzes hundreds of thousands of polymorphic markers generated by genomic rearrange- ments and provide the genome-wide genetic profile of the organism under study with no prior DNA sequence information [26]. To the best of our knowledge, sequence-based markers, i.e., DArTseq markers, are not used for the characterization of Peruvian rosewood germplasm. Therefore, it is very important to screen the rosewood germplasm with sequence-based markers for the com- prehensive conclusion of conservation genetics, germplasm collection, characterization and breeding strategies. Previous studies used PCR-based molecular markers to explore the genetic variation potential of rosewood germplasm from various parts of the world. Previous studies explored the genetic diversity of Brazilian rosewood germplasm through RAPD markers [27] and SSR markers [28]. Very recently, Guizado et al. [29] for the first time reported the characterization of Peruvian rosewood germplasm with molecular markers (ISSR markers) and confirmed the existence of a good level of genetic diversity in their germplasm. Genotyping by Sequencing (GBS) resulted in SNP and DArTseq markers have been found robust, high throughput and more informative compared to PCR-based markers [30,31]. As is obvious from the above-provided evidence, previous studies did not utilize whole-genome covering sequenced-based markers and the number of markers used in their study was very low. Therefore, the present investigation aimed to explore the in-depth genetic diversity and population structure of Peruvian rosewood germplasm using DArTseq markers.

2. Materials and Methods 2.1. Experimental Materials and Genomic DNA Extraction During this study, a total of 90 Peruvian rosewood accessions collected from eight localities were used as plant material (Table1, Figure1). These eight localities are present in the regions of Loreto and Ucayali, in the Peruvian Amazon which is considered the main habitats of rosewood in Peru. Among these eight, three localities are in the vicinity of Iquitos city, two of them accessible by road, and one on the margin of the Amazonas River. One population collected from Allpahuayo is close to the Allpahuayo–Mishana National Reserve. Populations from localities Zungarococha, Mayriricay, Nanay, Tamshiy- acu and Santa Marta are located within private estates, while populations collected from Huajoya and Maria de Huajoya, are present within native community lands. The Zungaro- cocha, Allpahuayo and Mairirircay plantations resulted from botanical seeds of natural Forests 2021, 12, 197 4 of 17

trees identified from the Tamshiyacu area. The purpose of zungarococha plantation was teaching, since it is a part of the Agronomy Faculty of the National University of the Peruvian Amazon. With regard to the Allpahuayo plantation, its purpose was to evaluate the development of this species in sandy soils and subsequently, essential oil analyses are performed. This plantation is conserved by the Peruvian Amazon Research Institute. Finally, the Mairiricay plantation was carried out by PEDICP (Binational Special Project for the Integral Development of the Putumayo River Basin) as part of an implementation project. To conserve rosewood populations, a pilot plantation project was started 25 years ago in the perimeter zone of the Allpahuayo National, Reserve by The Instituto de Inves- tigaciones de la Amazonía Peruana (IIAP). Zungarococha, Allpahuayo and Mairirircay populations are plantations from material originating from Tamshiyacu. These rosewood plantations are now 25, 20 and 15 years old, respectively. To isolate plant DNA, healthy and non-damaged leaves from all the rosewood accessions were separately collected and packaged into ice. All samples were then transported and preserved at −20 ◦C until DNA extraction in the laboratory of “Special- ized Unit of Biotechnology of the Research Center of Natural Resources of the Ama- zon”. Genomic DNA from all samples was extracted following the protocol proposed by Castro et al. [32] and a specific protocol suggested by Diversity Arrays Technology (available at https://www.diversityarrays.com/orderinstructions/plant-dna-extraction- protocol-for-dart/( accessed on 13 October 2020)). Genomic DNA quantification was performed with agarose gel (0.80%) and confirmed by spectrophotometry using Nanodrop 2000c (Thermo Scientific, Waltham, MA, USA). The DNA concentration of all rosewood samples was adjusted to a 50 ng·µL−1 for the purpose of genotyping by sequencing (GBS) analysis. The samples were prepared and sent to the Diversity Array Technology Pty, Ltd., Bruce, Australia, for DArTseq analyses of GBS (www.diversityarrays.com( accessed on 13 October 2020)).

2.2. Genotyping by Sequencing for DArTseq Markers DArTseq technology is a genome complexity reduction method based on a next- generation sequencing platform [33]. DArTseq assisted the selection of genomic fractions corresponding to active genes predominantly [34]. DNA samples were processed via Digestion/ligation reactions following the method of Kilian et al. [35]. A total of 30 PCR cycles were performed to amplify mixed fragments (PstI–MseI). More description about DArTseq markers analysis can be found in earlier studies [34–36].

2.3. Statistical Analysis 2.3.1. DArTseq Markers Analysis DArTsoft v.7.4.7 (DArT P/L, Canberra, Australia) was implemented to analyze all the images of DArTseq platform. Scoring of DArTseq markers was performed in a binary fashion, where 1 represents presence and 0 represents absence in the genomic representation of the restriction fragment of each sample [34–36]. Parameters like polymorphism information content (PIC), call rate, and reproducibility were considered during the screening of the markers. All those DArTseq markers were ignored having PIC value, reproducibility and call rate lower than 0.10, 100% and 0.80% to avoid false inferences. Forests 2021, 12, 197 5 of 17

Table 1. Passport data of 90 rosewood accessions collected from eight geographical localities of Peruvian Amazon.

Sr. No Genotype Name Region Province District Village Latitude Longitude Altitude 1 Nanay-1 Loreto Alto Nanay Santa maria del Nanay Quebrada Curaca 9,551,691 638,610 152 2 Nanay-2 Loreto Alto Nanay Santa maria del Nanay Santa maria del nanay 9,569,683 644,419 106 3 Nanay-3 Loreto Alto Nanay Santa maria del Nanay Santa maria del nanay 9,569,689 644,389 109 4 Nanay-4 Loreto Alto Nanay Santa maria del Nanay Santa maria del nanay 9,569,727 644,387 106 5 Nanay-5 Loreto Alto Nanay Santa maria del Nanay Santa maria del nanay 9,569,721 644,391 99 6 Alpahuayo-1 Loreto Maynas San Juan Bautista Alpahuayo 9,561,154 675,470 158 7 Alpahuayo-2 Loreto Maynas San Juan Bautista Alpahuayo 9,561,182 675,477 148 8 Alpahuayo-3 Loreto Maynas San Juan Bautista Alpahuayo 9,561,208 675,492 144 9 Alpahuayo-4 Loreto Maynas San Juan Bautista Alpahuayo 9,561,236 675,505 148 10 Alpahuayo-5 Loreto Maynas San Juan Bautista Alpahuayo 9,561,247 675,500 142 11 Alpahuayo-6 Loreto Maynas San Juan Bautista Alpahuayo 9,561,262 675,512 141 12 Alpahuayo-7 Loreto Maynas San Juan Bautista Alpahuayo 9,561,300 675,527 138 13 Zungarococha-1 Loreto Maynas San Juan Bautista Zungarococha 9,576,628 681,106 113 14 Zungarococha-2 Loreto Maynas San Juan Bautista Zungarococha 9,576,631 681,105 115 15 Zungarococha-3 Loreto Maynas San Juan Bautista Zungarococha 9,576,625 681,115 116 16 Zungarococha-4 Loreto Maynas San Juan Bautista Zungarococha 9,576,650 681,100 114 17 Tamshiyacu-1 Loreto Maynas Fernando Lores Tamshiyacu 9,559,735 706,059 112 18 Tamshiyacu-2 Loreto Maynas Fernando Lores Tamshiyacu 9559,801 706,144 110 19 Tamshiyacu-3 Loreto Maynas Fernando Lores Tamshiyacu 9,559,783 706,148 120 20 Tamshiyacu-4 Loreto Maynas Fernando Lores Tamshiyacu 9,559,741 706,087 123 21 Tamshiyacu-5 Loreto Maynas Fernando Lores Tamshiyacu 9,559,669 706,071 111 22 Tamshiyacu-6 Loreto Maynas Fernando Lores Tamshiyacu 9,560,651 705,900 125 23 Tamshiyacu-7 Loreto Maynas Fernando Lores Tamshiyacu 9,560,660 705,877 105 24 Tamshiyacu-8 Loreto Maynas Fernando Lores Tamshiyacu 9,560,676 705,862 116 25 Tamshiyacu-9 Loreto Maynas Fernando Lores Tamshiyacu 9,560,681 705,840 121 26 Tamshiyacu-10 Loreto Maynas Fernando Lores Tamshiyacu 9,559,356 706,026 119 27 Tamshiyacu-11 Loreto Maynas Fernando Lores Tamshiyacu 9,559,220 706,283 129 28 Tamshiyacu-12 Loreto Maynas Fernando Lores Tamshiyacu 9,559,223 706,274 112 29 Tamshiyacu-13 Loreto Maynas Fernando Lores Tamshiyacu 9,559,205 706,296 115 30 Tamshiyacu-14 Loreto Maynas Fernando Lores Tamshiyacu 9,559,076 706,243 108 31 Tamshiyacu-15 Loreto Maynas Fernando Lores Tamshiyacu 9,559,096 706,281 119 32 Tamshiyacu-16 Loreto Maynas Fernando Lores Tamshiyacu 9,559,092 706,266 115 33 Tamshiyacu-17 Loreto Maynas Fernando Lores Tamshiyacu 9,559,076 706,269 110 34 Mairiricay-1 Loreto Putumayo Putumayo Mairiricay 9,726,985 760,695 136 Forests 2021, 12, 197 6 of 17

Table 1. Cont.

Sr. No Genotype Name Region Province District Village Latitude Longitude Altitude 35 Mairiricay-2 Loreto Putumayo Putumayo Mairiricay 9,726,991 760,701 132 36 Mairiricay-3 Loreto Putumayo Putumayo Mairiricay 9,726,988 760,714 134 37 Mairiricay-4 Loreto Putumayo Putumayo Mairiricay 9,727,009 760,707 132 38 Mairiricay-5 Loreto Putumayo Putumayo Mairiricay 9,727,008 760,702 131 39 Mairiricay-6 Loreto Putumayo Putumayo Mairiricay 9,726,999 760,690 130 40 Mairiricay-7 Loreto Putumayo Putumayo Mairiricay 9,726,978 760,714 125 41 Mairiricay-8 Loreto Putumayo Putumayo Mairiricay 9,726,981 760,726 126 42 Mairiricay-9 Loreto Putumayo Putumayo Mairiricay 9,726,972 760,715 125 43 Mairiricay-10 Loreto Putumayo Putumayo Mairiricay 9,726,971 760,716 127 44 Mairiricay-11 Loreto Putumayo Putumayo Mairiricay 9,726,971 760,713 123 45 Mairiricay-12 Loreto Putumayo Putumayo Mairiricay 9,726,982 760,719 128 46 Mairiricay-13 Loreto Putumayo Putumayo Mairiricay 9,727,003 760,729 124 47 Mairiricay-14 Loreto Putumayo Putumayo Mairiricay 9,726,994 760,726 126 48 Mairiricay-15 Loreto Putumayo Putumayo Mairiricay 9,727,007 760,725 124 49 Santamarta-1 Ucayali Atalaya Masisea Santa Marta 8,980,940 604,385 171 50 Santamarta-2 Ucayali Atalaya Masisea Santa Marta 8,980,933 604,388 169 51 Santamarta-3 Ucayali Atalaya Masisea Santa Marta 8,980,925 604,386 170 52 Santamarta-4 Ucayali Atalaya Masisea Santa Marta 8,980,934 604,388 169 53 Santamarta-5 Ucayali Atalaya Masisea Santa Marta 8,980,923 604,387 172 54 Santamarta-6 Ucayali Atalaya Masisea Santa Marta 8,980,943 604,348 171 55 Santamarta-7 Ucayali Atalaya Masisea Santa Marta 8,981,608 604,180 171 56 Santamarta-8 Ucayali Atalaya Masisea Santa Marta 8,981,590 604,184 171 57 Santamarta-9 Ucayali Atalaya Masisea Santa Marta 8,981,587 604,200 173 58 Santamarta-10 Ucayali Atalaya Masisea Santa Marta 8,981,586 604,182 171 59 Santamarta-11 Ucayali Atalaya Masisea Santa Marta 8,981,588 604,231 174 60 Santamarta-12 Ucayali Atalaya Masisea Santa Marta 8,981,574 604,258 176 61 Santamarta-13 Ucayali Atalaya Masisea Santa Marta 8,981,667 604,622 174 62 Santamarta-14 Ucayali Atalaya Masisea Santa Marta 8,981,668 604,623 174 63 Santamarta-15 Ucayali Atalaya Masisea Santa Marta 8,981,674 604,632 175 64 Santamarta-16 Ucayali Atalaya Masisea Santa Marta 8,981,978 604,874 177 65 Santamarta-17 Ucayali Atalaya Masisea Santa Marta 8,981,965 604,878 175 66 Santamarta-18 Ucayali Atalaya Masisea Santa Marta 8,981,959 604,892 175 67 Santamarta-19 Ucayali Atalaya Masisea Santa Marta 8,981,528 604,688 172 68 Santamarta-20 Ucayali Atalaya Masisea Santa Marta 8,980,586 604,483 164 69 Mariadehuajoya-1 Loreto Maynas Napo Maria de Huajoya 9,838,429 536,797 120 Forests 2021, 12, 197 7 of 17

Table 1. Cont.

Sr. No Genotype Name Region Province District Village Latitude Longitude Altitude 70 Mariadehuajoya-2 Loreto Maynas Napo Maria de Huajoya 9,835,376 537,866 125 71 Mariadehuajoya-3 Loreto Maynas Napo Maria de Huajoya 9,833,880 535,209 116 72 Mariadehuajoya-4 Loreto Maynas Napo Maria de Huajoya 9,835,834 531,637 121 73 Mariadehuajoya-5 Loreto Maynas Napo Maria de Huajoya 9,838,277 528,614 118 74 Mariadehuajoya-6 Loreto Maynas Napo Maria de Huajoya 9,841,544 530,843 118 75 Mariadehuajoya-7 Loreto Maynas Napo Maria de Huajoya 9,839,223 533,377 123 76 Mariadehuajoya-8 Loreto Maynas Napo Maria de Huajoya 9,838,429 535,515 140 77 Mariadehuajoya-9 Loreto Maynas Napo Maria de Huajoya 9,841,788 535,393 135 Mariadehuajoya- 78 Loreto Maynas Napo Maria de Huajoya 9,840,811 537,164 129 10 79 Huajoya-1 Loreto Maynas Napo Huajoya 9,852,750 540,889 146 80 Huajoya-2 Loreto Maynas Napo Huajoya 9,851,987 543,454 152 81 Huajoya-3 Loreto Maynas Napo Huajoya 9,852,140 545,255 134 82 Huajoya-4 Loreto Maynas Napo Huajoya 9,854,918 544,828 142 83 Huajoya-5 Loreto Maynas Napo Huajoya 9,855,834 543,179 127 84 Huajoya-6 Loreto Maynas Napo Huajoya 9,855,010 539,087 131 85 Huajoya-7 Loreto Maynas Napo Huajoya 9,854,949 537,744 135 86 Huajoya-8 Loreto Maynas Napo Huajoya 9,856,109 539,912 145 87 Huajoya-9 Loreto Maynas Napo Huajoya 9,855,651 543,576 155 88 Huajoya-10 Loreto Maynas Napo Huajoya 9,854,430 544,858 149 89 Huajoya-11 Loreto Maynas Napo Huajoya 9,852,873 547,362 138 90 Huajoya-12 Loreto Maynas Napo Huajoya 9,851,040 546,660 151 Forests 2021, 12, x FOR PEER REVIEW 6 of 16 Forests 2021, 12, 197 8 of 17

FigureFigure 1.1. CollectionCollection points of eight location of of Peruvian Peruvian rosewood rosewood germplasm. germplasm.

2.3.2.To Genetic isolate Diversity plant DNA, Analyses healthy and non-damaged leaves from all the rosewood acces- sionsA were total separately of 11,332 collected DArTseq and markers packaged were into obtained ice. All by samples DArTseq were profiling then transported of 90 rose- woodand preserved accessions. at A−20 total °C ofuntil 7485 DNA high-quality extraction markers in the werelaboratory retained of for“Speci furtheralized analysis Unit of by filteringBiotechnology the total of datasetthe Research accounting Center markers of Natural with Resources less than 5%of the missing Amazon”. data, PICGenomic value ofDNA 0.10 from to 0.50, all samples call rate was 0.80 extracted to 1 and following 100% reproducibility. the protocol proposed Various diversity by Castro indices et al. [32] like theand observed a specific number protocol of alleles suggested (Na), theby effectiveDiversity number Arrays of Technology alleles (Ne), (available and unbiased at expectedhttps://www.diversityarrays.com heterozygosity (uHe) for/orderinstructions/plant-d eight localities were investigatednaextraction-protocol-for through GenAlEx 6.5 softwaredart/). Genomic [37]. Genetic DNA quantification distance is a measurementwas performed of with genetic agarose divergence gel (0.80%) between and eithercon- speciesfirmed by or spectrophotometry populations within using a species Nanodrop [38]. 2000c To investigate (Thermo geneticallyScientific, Waltham, distinct acces-MA, sionsUSA). from The PeruvianDNA concentration rosewood of germplasm, all rosewood Jaccard’s samples coefficient was adjusted of genetic to a 50 dissimilarity ng·μL−1 for wasthe purpose calculated of genotyping using a vegan by sequencing package of (GBS) R statistical analysis. software The samples [39]. were GenAlEx prepared v6.5 and soft- waresent to [37 the] was Diversity also used Array for Technology the investigation Pty, Lt ofd., principal Bruce, Australia, coordinate for analysis DArTseq (PCoA) analyses and analysisof GBS (www.diversityarrays.com). of molecular variance (AMOVA). The STRUCTURE software (version 2.3.4) was utilized to construct the population structure of the 90 rosewood accessions [40]. A total of2.2.1–10 Genotyping groups (K)by Sequencingwere set withfor DArTseq ten independent Markers runs for each K (50,000 burn-ins and 500,000DArTseq Markov technology Chain Monte is a genome Carlo generations) complexity withreduction no prior method information based on on a next-gen- the origin oferation individuals. sequencing The platform proposed [33]. methodology DArTseq assisted of Evanno the selection et al. [41 of] genomic was implemented fractions cor- for theresponding investigation to active of the genes most predominantly probable number [34]. DNA of subpopulations samples were (processed∆K). Later, via structure Diges- evaluatedtion/ligation results reactions were following processed the with method STRUCTURE of Kilian HARVESTERet al. [35]. A total v.0.9.94 of 30 to PCR investigate cycles thewere most performed favorable to K valueamplify [42 ].mixed The pophelperfragments and (PstI–MseI). R package More was useddescription to visualize about the mostDArTseq favorable markers∆K[ analysis43]. To can explore be found the diversity in earlier among studies STRUCTURE-based [34–36]. populations, various diversity indices were investigated through GenAlEx 6.5 software [37] and Jac- card’s2.3. Statistical coefficients Analysis of genetic dissimilarity were also calculated using a vegan package of R statistical2.3.1. DArTseq software Markers (39). TheAnalysis coefficient of differentiation (Fst) is a measure of population differentiation due to genetic structure. The Fst is directly related to the variations in DArTsoft v.7.4.7 (DArT P/L, Canberra, Australia) was implemented to analyze all the allele frequency among populations and, conversely, to the degree of resemblance among images of DArTseq platform. Scoring of DArTseq markers was performed in a binary individuals within populations [44]. The coefficient of differentiation (Fst) was evaluated fashion, where 1 represents presence and 0 represents absence in the genomic representa- from structure software and gene flow among structure-based populations was calculated tion of the restriction fragment of each sample [34–36]. Parameters like polymorphism according to Fst–methodology described by Slatkin [45] and Slatkin and Barton [46]. To information content (PIC), call rate, and reproducibility were considered during the explore the relationship among 90 rosewood accessions, the Jaccard coefficient of genetic screening of the markers. All those DArTseq markers were ignored having PIC value, re- dissimilarity was used to investigate neighbor-joining analysis through an ape package of producibility and call rate lower than 0.10, 100% and 0.80% to avoid false inferences. R statistical software [39].

Forests 2021, 12, 197 9 of 17

3. Results DArTseq Proﬁling by GBS The distribution of the PIC values of the ﬁltered dataset of 7485 markers is provided in Figure2. The mean, maximum, and minimum PIC values of 0.322, 0.50, and 0.10 were revealed for the whole rosewood germplasm panel. Similarly mean, maximum, and Forests 2021, 12, x FOR PEER REVIEW 8 of 16 minimum call rate values of 0.928%, 1.00%, and 0.80% were observed through the rosewood germplasm panel of 90 accessions (Figure2).

FigureFigure 2.2. FrequencyFrequency histogramhistogram revealing call raterate andand polymorphismpolymorphism informationinformation contentcontent (PIC)(PIC) valuesvalues ofof thethe appliedapplied DArTseqDArTseq markers.markers. ((AA):): callcall raterate ofof 74857485 DArTseqDArTseq markers; markers; ( B(B):): PIC PIC value value of of 7485 7485 DArTseq DArTseq markers markers

During thisthis study, study, various various diversity diversity indices indices like thelike observed the observed number number of alleles of (1.962),alleles the(1.962), effective the effective number number of alleles of alleles (1.669), (1.669) unbiased, unbiased expected expected heterozygosity heterozygosity (0.411), (0.411), and polymorphism (93.51%) showed the presence of a great level of genetic variation in the and polymorphism (93.51%) showed the presence of a great level of genetic variation in rosewood germplasm panel of 90 accessions (Table2). Among the studied eight popu- the rosewood germplasm panel of 90 accessions (Table 2). Among the studied eight pop- lations, the Mairiricay population reflected higher values for various diversity indices ulations, the Mairiricay population reflected higher values for various diversity indices (Table2) like the observed number of alleles (2.00), an effective number of alleles (1.71), (Table 2) like the observed number of alleles (2.00), an effective number of alleles (1.71), unbiased expected heterozygosity (0.426), polymorphism (100%) and Jaccard’s coefficient unbiased expected heterozygosity (0.426), polymorphism (100%) and Jaccard’s coefficient of genetic dissimilarity (0.585). Among eight populations, Zungarococha was found least of genetic dissimilarity (0.585). Among eight populations, Zungarococha was found least diverse by reflecting minimum values for calculated diversity indices (Table2). Mean Jac- diverse by reflecting minimum values for calculated diversity indices (Table 2). Mean Jac- card’s coefficient of genetic dissimilarity among 90 rosewood accessions was 0.421, while card’s coefficient of genetic dissimilarity among 90 rosewood accessions was 0.421, while highest Jaccard’s coefficient of genetic dissimilarity (0.828) was present between rosewood highest Jaccard’s coefficient of genetic dissimilarity (0.828) was present between rosewood accessions Tamshiyacu-2 and Mairiricay-15 respectively. Minimum Jaccard’s coefficient of geneticaccessions dissimilarity Tamshiyacu-2 was (0.261)and Mairiricay-15 present between respectively. rosewood Minimum accessions Jaccard’s Zungarococha-1 coefficient andof genetic Zungarococha-4. dissimilarity The was results (0.261) of presen AMOVAt between reflected rosewood the presence accessions of greater Zungarococha- variations within1 and Zungarococha-4. populations (75%) The compared results of to AMOVA among the reflected populations the presence (25%) (Table of greater3). The variations genetic structurewithin populations of the rosewood (75%) compared germplasm to wasamong separated the populations into three (25%) populations (Table 3). as The proposed genetic bystructure∆K peak of attheK rosewood= 3 (Figure germplasm S1). STRUCTURE was sepa softwarerated into divided three populations studied germplasm as proposed into threeby ΔK main peak subpopulations at K = 3 (Figure on S1). the STRUCTURE basis of their software collection divided points studied (Figure3 germplasm). A total of into 37, 20three and main 22 accessions subpopulations were clusteredon the basis in subpopulationsof their collection A, points B and (Figure C respectively, 3). A total on of the37, basis20 and of 22 membership accessions coefficients were clustered of either in subpopulations 75% or more than A, 75%B and within C respectively, the same structure on the populationbasis of membership group. A totalcoefficients of 11 rosewood of either accessions75% or more revealed than 75% membership within the coefficients same structure less thanpopulation 75% and group. were A considered total of 11 as rosewood unclassified accessions subpopulations. revealed Diversitymembership indices coefficients among STRUCTUREless than 75% evaluated and were subpopulations considered as revealed unclassified the existence subpopulations. of higher Diversity gene flow indices (1.557) andamong mean STRUCTURE Jaccard’s coefficient evaluated of subpopulations genetic dissimilarity revealed (0.465) the existence for subpopulation of higher gene A, while flow subpopulation(1.557) and mean B revealed Jaccard’s the coefficient highest levelof genetic of coefficient dissimilarity of differentiation (0.465) for subpopulation (Fst) (0.501) and A, minimumwhile subpopulation values for variousB revealed diversity the highest indices level (Table of4 ).coefficient The neighbor-joining of differentiation analysis (Fst) (0.501) and minimum values for various diversity indices (Table 4). The neighbor-joining analysis divided the whole studied germplasm into three populations on the basis of their collection points (Figure 4). The PCoA clearly supported the clustering of STRUCTURE and neighbor-joining-based clustering and separated the Santamarta population from the rest of the populations (Figure 5).

Forests 2021, 12, 197 10 of 17

divided the whole studied germplasm into three populations on the basis of their collection points (Figure4). The PCoA clearly supported the clustering of STRUCTURE and Forests 2021, 12, x FOR PEER REVIEW 9 of 16 neighbor-joining-based clustering and separated the Santamarta population from the rest of the populations (Figure5).

TableTable 2.2.Diversity Diversity indicesindices for for Peruvian Peruvian rosewood rosewood populations populations on on the the basis basis of of geographical geographical localities. localities. Population Na Ne uHe %P GD Population Na Ne uHe %P GD Alpahuayo 1.980 1.659 0.410 98.68% 0.501 Alpahuayo 1.980 1.659 0.410 98.68% 0.501 Huajoya 1.999 1.694 0.418 99.96% 0.482 MairiricayHuajoya 2.00 1.999 1.71 1.694 0.426 0.418 100% 99.96% 0.585 0.482 MariadehuajoyaMairiricay 1.9972.00 1.678 1.71 0.413 0.426 99.83%100% 0.4050.585 NanayMariadehuajoya 1.902 1.997 1.632 1.678 0.403 0.413 93.59% 99.83% 0.312 0.405 SantamartaNanay 2.00 1.902 1.698 1.632 0.415 0.403 68.18% 93.59% 0.316 0.312 TamshiyacuSantamarta 2.002.00 1.691 1.698 0.414 0.415 99.99%68.18% 0.3360.316 ZungarocochaTamshiyacu 1.8192.00 1.590 1.691 0.387 0.414 87.88%99.99% 0.4340.336 OverallZungarococha 1.962 1.819 1.669 1.590 0.411 0.387 93.51% 87.88% 0.421 0.434 Na: observed numberOverall of alleles, Ne: number 1.962 of effective alleles,1.669 uHe:0.411 unbiased expected 93.51% heterozygosity, 0.421 %P: percent polymorphism, GD: Jaccard coefﬁcient of genetic dissimilarity. Na: observed number of alleles, Ne: number of effective alleles, uHe: unbiased expected heterozygosity, %P: percent polymorphism, GD: Jaccard coefficient of genetic dissimilarity. Table 3. Analysis of molecular variance for among and within populations of the studied rosewood accessions.Table 3. Analysis of molecular variance for among and within populations of the studied rosewood accessions. Source Df SS MS Est. Var. % Source Df SS MS Est. Var. % AmongAmong Population Population 7 7 38,364.847 38,364.847 5480.692 5480.692 393.893 393.893 25% 25% Within Population 82 98,123.975 1196.634 1196.634 75% Within Population 82 98,123.975 1196.634 1196.634 75% Total 89 136,488.822 - 1590.527 100% Total 89 136,488.822 - 1590.527 100%

FigureFigure 3.3. ClusteringClustering ofof thethe 9090 rosewoodrosewood accessionsaccessions viavia structure-basedstructure-based clusteringclustering algorithmalgorithm withwith DArTseqDArTseq markers. markers.

TableTable 4.4. GeneticGenetic diversity diversity indices indices for forthe STRUCTURE- the STRUCTURE-basedbased populations populations of Peruvian of Peruvian rosewood rose- woodgermplasm. germplasm.

PopulationPopulation NeNe GDGD FstFst NmNm Population A 1.703 0.465 0.243 1.557 Population A 1.703 0.465 0.243 1.557 PopulationPopulation B B 1.68 1.68 0.407 0.407 0.501 0.501 0.498 0.498 PopulationPopulation C C 1.702 1.702 0.4410.441 0.4250.425 0.6760.676 Ne:Ne: NumberNumber of of effective effective alleles, alleles, GD: GD: Jaccard Jaccard coefficient coefficient of genetic of dissimilarity,genetic dissimilarity, Fst: coefficient Fst: ofcoefficient differentiation, of Nm:differentiation, Gene flow. Nm: Gene flow.

Forests 2021, 12, 197 11 of 17 Forests 2021, 12, x FOR PEER REVIEW 10 of 16

Forests 2021, 12, x FOR PEER REVIEW 11 of 16

FigureFigure 4. Neighbor4. Neighbor joining-based joining-based clustering of of the the 90 90 rosewood rosewood accessions. accessions.

FigureFigure 5. Principal 5. Principal coordinate coordinate analysis analysis (PCoA)-based (PCoA)-based clustering clustering of the 90 of rosewood the 90 rosewood accessions.4. accessions. Discussion.

Rosewood is an endangered plant of the Amazon region, famous for its essential oil. However, there is a scarcity of information about the characterization of Peruvian rosewood germplasm using GBS-derived DArTseq markers. Therefore, an effort was made through this study to explore the genetic diversity and population structure of Peruvian rosewood germplasm through DArTseq markers. The molecular characterization of Pe- ruvian rosewood germplasm with DArTseq markers explored genetic variations in the studied germplasm (Table 2). Diversity indices calculated in this study showed the existence of genetic variations in the Peruvian rosewood germplasm. As rosewood is now a vulnerable species in Peru [14], strategies should be developed for the conservation of this economically important plant. Previous studies by Angrizani et al. [28] and Santos et al. [47] did not calculate various diversity indices like the observed number of alleles, and the number of effective alleles. However, the mean and range of polymorphism (%) in Peruvian amazon rosewood populations was found higher than reported by Santos et al. [47] in Brazilian rosewood populations. The possible reasons for the existence of higher values for various diversity indices in this study might be due to either higher efficiency of DArTseq marker system in exploring the genetic diversity or the experimental materials are of diverse nature. Moreover, we used thousands of markers for genetic diversity analysis compared to gel-based markers which are in hundreds and cannot provide deep information. Among the studied eight rosewood populations, the Mairiricay population was found most diverse by reflecting higher values for calculated parameters, while the Zuna- garococha population was found least diverse population (Table 2). Therefore, accessions from the Mairiricay population can be suggested for future rosewood germplasm conservation and breeding activities. Genetic distance is a degree of genomic differences between species or populations and it is calculated by some numerical method [38–48]. Very recent studies confirmed genetic distance as a valuable criterion for the selection of parents that can be used in breeding activities [49,50]. Germplasm resources proposing the highest level of genetic distance must be properly conserved and utilize in future breeding programs for their improvement [29]. During this study, the maximum Jaccard coefficient of genetic dissimilarity was present between Tamshiyacu-2 and Mairiricay-15. Therefore, Forests 2021, 12, 197 12 of 17

4. Discussion Rosewood is an endangered plant of the Amazon region, famous for its essential oil. However, there is a scarcity of information about the characterization of Peruvian rosewood germplasm using GBS-derived DArTseq markers. Therefore, an effort was made through this study to explore the genetic diversity and population structure of Peruvian rosewood germplasm through DArTseq markers. The molecular characterization of Peruvian rosewood germplasm with DArTseq markers explored genetic variations in the studied germplasm (Table2). Diversity indices calculated in this study showed the existence of genetic variations in the Peruvian rosewood germplasm. As rosewood is now a vulnerable species in Peru [14], strategies should be developed for the conservation of this economically important plant. Previous studies by Angrizani et al. [28] and Santos et al. [47] did not calculate various diversity indices like the observed number of alleles, and the number of effective alleles. However, the mean and range of polymorphism (%) in Peruvian amazon rosewood populations was found higher than reported by Santos et al. [47] in Brazilian rosewood populations. The possible reasons for the existence of higher values for various diversity indices in this study might be due to either higher efficiency of DArTseq marker system in exploring the genetic diversity or the experimental materials are of diverse nature. Moreover, we used thousands of markers for genetic diversity analysis compared to gel-based markers which are in hundreds and cannot provide deep information. Among the studied eight rosewood populations, the Mairiricay population was found most diverse by reflecting higher values for calculated parameters, while the Zunagaro- cocha population was found least diverse population (Table2). Therefore, accessions from the Mairiricay population can be suggested for future rosewood germplasm conservation and breeding activities. Genetic distance is a degree of genomic differences between species or populations and it is calculated by some numerical method [38–48]. Very recent studies confirmed genetic distance as a valuable criterion for the selection of parents that can be used in breeding activities [49,50]. Germplasm resources proposing the highest level of genetic distance must be properly conserved and utilize in future breeding programs for their improvement [29]. During this study, the maximum Jaccard coefficient of genetic dissimilarity was present between Tamshiyacu-2 and Mairiricay-15. Therefore, these accessions might be suggested for rosewood conservation and utilization in future breeding strategies. The analysis of molecular variance (AMOVA) is performed to investigate the level of genetic differentiation among studied populations. The AMOVA results revealed that higher genetic variations in rosewood germplasm were due to differences within the populations and these results were found in line with previous reports [29–45]. Santos et al. [46] used RAPD markers for the characterization of central Brazilian Amazon germplasm and found higher genetic variations (76.6%) within populations than among (23.4%) populations. Very recently, Guizado et al. [29] characterized the Peruvian rosewood using ISSR markers and found higher variations within populations (98.1%) than among (1.9%) populations. A previous study concluded that long-term natural selection and geographical isolation allowed the local population to conserve a specific genotype, thereby increasing the genetic variations between populations [51]. STRUCTURE, neighbor-joining analysis, and PCoA were used as clustering algorithms to elucidate the population structure of Peruvian rosewood germplasm. STRUCTURE algorithms were given more preference among these clustering algorithms as they showed more robustness in previous research works [52,53]. STRUCTURE software separated the whole germplasm into three main subpopulations (A, B, C) on the basis of their geographical localities (Figure3). Accessions belonging to Mairiricay, Mariacdehuajoya, Huajoya, and Nanay localities were clustered together by making subpopulation A. It is clearly under- standable from Figure1 that Mariacdehuajoya, Huajoya, and Mairiricay are close to each other. Therefore, these populations clustered within the same subpopulation of structure analysis. There was a possibility of frequent gene flow among these populations which resulted in genetic similarity and their grouping under the same population. To support this Forests 2021, 12, 197 13 of 17

hypothesis, various diversity indices were calculated among STRUCTURE-based subpopulation (Table4). Results confirmed the existence of higher genetic diversity, genetic distance and gene flow in subpopulation A. A total of five accessions from the Nanay location were used as plant material. However, only two accessions (Nanay-4, Nanay-5) showed a membership coefficient of more than 75% and grouped in subpopulations A. Nanay population is located away from Mariacdehuajoya and Huajoya populations. However, the Nanay population clustered with these populations in STRUCTURE-based clustering. Mariacdehuajoya and Huajoya populations belong to the Napo basin which is next to the Nanay basin which contains the Nanay population. There is a great possibility of gene flow between Napo basin and Nanay basin that allows the clustering of Nanay population with Mariacdehuajoya and Huajoya population in structure analysis. Subpopulation B was found to be homogeneous as it clustered all accessions (a total of 20 accessions) belonging to the Santamarta location. The Santamarta population showed low gene flow and a higher coefficient of differentiation (Fst) than the rest of the populations (Table4), which is possibly due to the greater geographical distance and isolation of this stand from the other localities. Santos et al. [47] observed the presence of higher gene flow among Brazilian rosewood populations close to each other and concluded that gene flow will decrease with the increase in geographic distance. Subpopulation C clustered a total of 22 rosewood accessions from Tamshiyacu, Alpahuayo, and Zungarococha localities. Clustering of Zunagarococha, Allpahuayo, and Tamshiyacu was expected because Zunagarococha and Allpahuayo were planted from material originating from the wild population of Tamshiyacu. It was interest- ing that a total of 11 rosewood accessions (three from Nanay and eight from Tamshiyacu populations) did not show genetic similarity with the above three populations. All of these accessions were considered unclassified accessions as they revealed membership coefficients Q < 75%. Grouping of rosewood accession in this study was also supported by our very recent study in which Peruvian rosewood germplasm was characterized with an ISSR marker [29]. The neighbor-joining analysis also supported the clustering of STRUC- TURE software and grouped the whole germplasm into three populations on the basis of their collection points (Figure5). Similar to STRUCTURE clustering, accessions from the Santamarta population were grouped together and confirmed their genetic dissimilarity to the rest of the populations. In a similar way to STRUCTURE clustering, populations from Mariacdehuajoya, Huajoya and Nanay localities were present very close to each other in PCoA-based clustering (Figure5). Similarly, accessions from the Santamarta population were clustered together and made their separate population as observed in STRUCTURE and neighbor-joining analysis.

Conservation Implications Research activities about the genetic diversity of endangered plants are very important because they provide a deep insight into their potential to combat environmental changes. The management of species diversity is regarded as one of the key aspects of current species genetic diversity investigation and conservation strategies [17,54,55]. However, limited information is documented about the conservation genetics and population structure assessment of endangered species. Previous studies recommended that research activities related to in vitro propagation and seed viability can be very effective for the conservation of endangered species [56,57]. Therefore, studies should be conducted related to seed viability and in vitro propagation of rosewood for the conservation perspectives. Moreover, efforts should be made to place rosewood in botanical gardens as well. The findings of this study showed a relatively high genetic diversity and low coefficient of differentiation (Fst) in population A of STRUCTURE clustering and explored its potential for conservation implications, and breeding activities to improve the genetic basis of rosewood. During this study, the AMOVA results confirmed that maximum variations in Peruvian rosewood germplasm are present within populations. Therefore, populations having high genetic diversity should be used for both ex situ and in situ germplasm collection and conservation aspects. Moreover, individuals from this population should be Forests 2021, 12, 197 14 of 17

used in reintroduction or reinforcement plans of rosewood. Results of this study also showed that population A reﬂected higher genetic diversity and may still maintain a relic of the ancient genetic structure as revealed by high genetic diversity and low genetic differentiation values. The greater level of genetic diversity and gene ﬂow in population A revealed that overexploitation and habitat fragmentation have not yet seriously affected the within-population diversity. Therefore, it is suggested that a restoration plan should be implemented utilizing population A. By considering the importance of threat to rosewood in Peruvian Amazon territory, The Instituto de Investigaciones de la Amazonía Peruana (IIAP) has started a pilot plantation project 25 years ago in the perimeter zone of the Allpahuayo National, Reserve. It is also suggested that a nursery or seed bank should be developed on an urgent basis by collecting the seeds from different geographic locations of the world where rosewood habitats are present. In the end, it is recommended that a combination of both in situ and ex situ conservation approaches would be the best strategy to conserve the valuable genetic resources of rosewood.

5. Conclusions This study provided deep insight into the genetic diversity and population structure of Peruvian rosewood. The Mairiricay population was found most diverse among eight localities. The results of AMOVA showed the presence of higher genetic diversity within populations. Tamshiyacu-2 and Mairiricay-15 accessions were found genetically distinct and can be suggested as candidate parents for future rosewood breeding activities. The implemented clustering algorithms, i.e., model-based structure, neighbor-joining analysis and principal coordinate analysis (PCoA) successfully separated the rosewood accessions based on their geographical locations. Genetic diversity indices revealed subpopulation A of the STRUCTURE algorithm as a genetically most diverse population and confirmed that overexploitation and habitat fragmentation have not yet seriously affected the within-population diversity in this population. Combining in situ and ex situ conservation approaches would be the best strategy to conserve the valuable genetic resources of rosewood. We are confident that the information provided here will be very helpful to the scientific community interested in rosewood management, conservation, and breeding activities.

Supplementary Materials: The following will be available online at https://www.mdpi.com/1999 -4907/12/2/197/s1, Figure S1: Delta K value proposing the presence of three sub-populations for the 90 rosewood accessions. Author Contributions: Methodology, F.S.B.; software, M.A.N. (Muhammad Azhar Nadeem) and E.H.; validation, F.S.B., S.J.V.G., S.E., M.A.N. (Muhammad Azhar Nadeem), and F.A.; formal analysis, M.A.N. (Muhammad Azhar Nadeem), E.H., M.Q.S.; investigation, S.J.V.G., F.A., M.A.N. (Muhammad Azhar Nadeem), M.A.; resources, J.C.C.G., F.S.B., G.C., S.H.Y. and J.L.M.d.A.; data curation, S.J.V.G., P.M.A.J. and E.T.C.; writing—original draft preparation, M.A.N. (Muhammad Azhar Nadeem) and F.A.; writing—review and editing, M.A.N. (Muhammad Amjad Nawaz), T.K., M.A., R.H., M.Q.S., S.E.,; visualization, F.S.B., G.C., S.H.Y., S.E.; supervision, F.S.B., J.C.C.G. All authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: All data required to conduct this study is provided within the manuscript. Acknowledgments: Authors are very grateful to Servicio Nacional Forestal y de Fauna Silvestre (SERFOR), Peru for providing the ﬁnancial support for the collection of germplasm (1360-2018- MINAGRI-SERFOR-CAF). Authors also pay their gratitude to Programa Nacional de Innovación Agraria (PNIA), Peru for providing a scientiﬁc internship to Stalin Juan Vasquez Guizado (156-2018- INIA-PNIA), at the Bolu Abant Izzet Baysal University, Bolu, Turkey. Forests 2021, 12, 197 15 of 17

Conﬂicts of Interest: The authors declare no conﬂict of interest.

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